class_docstr: Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of
  independent normally distributed random variables with means `0` follows a
  Cauchy distribution.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = Cauchy(torch.tensor([0.0]), torch.tensor([1.0]))
      >>> m.sample()  # sample from a Cauchy distribution with loc=0 and scale=1
      tensor([ 2.3214])
  
  Args:
      loc (float or Tensor): mode or median of the distribution.
      scale (float or Tensor): half width at half maximum.
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code: def variance(self):
      return torch.full(
          self._extended_shape(),
          inf,
          dtype=self.loc.dtype,
          device=self.loc.device,
      )
  
file_path: pytorch/torch/distributions/cauchy.py
imports: import math
  from numbers import Number
  import torch
  from torch import inf, nan
  from torch.distributions import constraints
  from torch.distributions.distribution import Distribution
  from torch.distributions.utils import broadcast_all
label_desc: Use image node_id 5 for calling the Cauchy obj's underlying member method code with example usage: obj.variance() and returns: torch